受大脑导航机制启发，用神经编码提升单图6自由度相机定位精度。
NeuroLoc: Encoding Navigation Cells for 6-DOF Camera Localization
- 模仿海马体细胞设计记忆模块，复现历史场景细节。
- 引入方向细胞机制，解决相似场景中的朝向混淆问题。
- 在姿态回归中加入3D网格中心预测，减少错误定位。
近年来，相机定位因其高效便捷被广泛应用于自主机器人导航。然而，在未知环境中，场景模糊、环境干扰和动态物体变化常导致定位失效。受生物脑导航机制（如网格细胞、位置细胞、头方向细胞）启发，我们提出一种新型神经生物学相机定位方法NeuroLoc。首先，设计基于位置细胞驱动的赫布学习模块，用于保存并回放历史信息，以恢复历史表征细节，缓解场景模糊问题。其次，借鉴头方向细胞机制，采用多头注意力嵌入实现内部方向学习，帮助在相似场景中还原真实朝向。最后，在姿态回归模块中加入3D网格中心预测，降低最终误判概率。我们在常用室内与室外基准数据集上评估了NeuroLoc。实验结果表明，该方法能在复杂环境中增强鲁棒性，并仅使用单张图像显著提升姿态回归性能。
原文摘要 · Abstract (English)
Recently, camera localization has been widely adopted in autonomous robotic navigation due to its efficiency and convenience. However, autonomous navigation in unknown environments often suffers from scene ambiguity, environmental disturbances, and dynamic object transformation in camera localization. To address this problem, inspired by the biological brain navigation mechanism (such as grid cells, place cells, and head direction cells), we propose a novel neurobiological camera location method, namely NeuroLoc. Firstly, we designed a Hebbian learning module driven by place cells to save and replay historical information, aiming to restore the details of historical representations and solve the issue of scene fuzziness. Secondly, we utilized the head direction cell-inspired internal direction learning as multi-head attention embedding to help restore the true orientation in similar scenes. Finally, we added a 3D grid center prediction in the pose regression module to reduce the final wrong prediction. We evaluate the proposed NeuroLoc on commonly used benchmark indoor and outdoor datasets. The experimental results show that our NeuroLoc can enhance the robustness in complex environments and improve the performance of pose regression by using only a single image.
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